System and method for estimating rating criteria values

ABSTRACT

Estimations of carbon dioxide (“CO2”) emission of an entity upon the condition of incomplete or missing data uses one or more algorithms implemented in a machine having a processor and a memory and data concerning the entity. The data is applied to an algorithm implemented as code executable in the processor. The algorithm produces a result that comprises an estimate of the CO2 emission of the entity. The CO2 emission estimate can be output to a user, and the underlying formula and data can inspected and optionally modified by users with suitable permissions. The CO2 emission estimate can be applied as a factor in a formula to compute a rating for the entity which can be output from the machine. Error estimates associated with the data used by the algorithm can be generated to provide improved estimates.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 13/629,186, filed Sep. 27, 2012, entitled “CO2 EstimatorModule,” now allowed, which is a continuation of U.S. patent applicationSer. No. 12/702,593, filed Feb. 9, 2010, entitled “System and Method forEstimating CO2 Emissions, now U.S. Pat. No. 8,321,234, which claims thebenefit of U.S. patent application Ser. No. 61/152,591, filed Feb. 13,2009, and entitled “System Including CO2 Emission Estimator Module,” allof which are hereby incorporated by reference in their respectiveentireties.

FIELD OF THE INVENTION

The present invention concerns the rating and benchmarking of companiesusing extrafinancial information such as environmental data. Moreparticularly, carbon dioxide emission data is estimated using aprogrammed machine and automatically presented through an interface inthe absence of data on this topic for the current reporting period orwhen complete information is not available.

BACKGROUND OF THE INVENTION

Depending on the sector and the company, CO2 emission data may beavailable in a timely manner, or may not be available at all. Across aspectrum of companies, the range of information disclosed and availablethrough corporate documents and to news reporting services can vary fromthorough to partial to none. When such data is available, it can becompiled (e.g., its components can be weighted) and presented as a valuethat is useful in rating a company relative to its peers. U.S. Pat. No.7,277,864, assigned to the present assignee, describes rating systemsthat can accommodate such extra-financial data, when present. However,improvements are needed in the art to address circumstances that are alltoo common in which incomplete or non-existent data provides no valuefor rating an entity such as a company. The present invention addressesthis problem using heuristics that provide multiple models forestimating CO2 total emission and for providing, automatically, anestimated value when a reported value is not available.

SUMMARY OF THE INVENTION

In accordance with one aspect of the invention, a method for outputtinga rating based upon an estimate of a carbon dioxide emission of anentity is implemented in a machine having a processor and a memory.According to this aspect, data is received at the machine concerning theentity. The data is applied to an algorithm implemented as codeexecutable in the processor. The algorithm produces a result thatcomprises an estimate of the carbon dioxide emission of the entity. Thecarbon dioxide emission estimate is applied as a factor in a formula tocompute a rating for the entity. The rating is output from the machine.

In accordance with another aspect of the invention, a method forestimating a carbon dioxide emission of an entity is implemented in amachine having a processor and a memory. In this method, data asdescribed above is received at the machine and applied to acode-implemented algorithm that produces an estimate of the carbondioxide emission of the entity. The estimated carbon dioxide emission isthen output from the machine.

Methods in accordance with further, optional aspects of the inventioncan have the algorithm apply data concerning an industry, sector, orsub-sector to which the entity belongs. Also, methods in accordance withfurther optional aspects can have the data that is applied to thealgorithm include both qualitative and quantitative data. Also,optionally, the estimated carbon dioxide emission can be made availableto a user through a user interface, and, if so, can be a result ofoperation of a data-fault module executing in the machine, that makesthe estimated carbon dioxide emission value available if the entity isdetermined to have missing or incomplete carbon dioxide emission data.In addition, more than one algorithm can be applied to the data so as toprovide discrete estimates of the carbon dioxide emission from eachalgorithm that are combined with one another, such as, by way ofexample, a simple average.

Another, optional feature of a method in accordance with the inventioncan include the additional steps of computing an error associated withthe algorithm(s) by applying the algorithm(s) to at least one additionalentity having a known carbon dioxide emission, and calculating adifference between the estimated carbon dioxide emission for theadditional entity and the known carbon dioxide emission. Any calculateddifference can be used to correct for error in the estimated carbondioxide emission for the entity.

In a further aspect, methods as described above can be further arrangedto permit users, through a user-interface, to customize thealgorithm(s).

In still a further aspect of the invention, a computer-implementedsystem combines plural modules that cooperate to output a carbon dioxideemission estimate in the absence of data or when complete data is notavailable for a given period (e.g., year). The carbon dioxide emissionestimate can be calculated in connection with the operation of a ratingprogram or system and utilized to supply a value or values that are usedin the calculation of a rating for an entity and so on.

These and other features, aspects and advantages of the invention can beappreciated from the following Description of Certain Embodiments of theInvention and the accompanying Drawing Figures.

DESCRIPTION OF THE DRAWING FIGURES

FIG. 1 is a schematic diagram illustrating at a high-level certainvariable types that can be used in estimating carbon production by anentity.

FIG. 2 is a chart showing a general trend of sectors with large carbonemissions having better reporting that sectors with comparatively lowercarbon emissions, and further illustrates, for calendar year 2006 data,the percentage of companies reporting by sector.

FIG. 3 is block diagram of a computer system 300 configured foremployment of a method in accordance with one embodiment of theinvention.

FIG. 4 is a block diagram showing a set of modules that cooperate toselectively provide a carbon dioxide emission estimate to a user or to arating program.

FIGS. 5A, 5B and 5C show a portion of a user-interface associated with arating program as may be seen by a user.

FIG. 6, shows a portion of a user interface that presents detailsconcerning a parameter of a main company under investigation includingits value and the basis for its calculation.

FIG. 7 can be part of the user interface of FIG. 6 or a separate pageselectable by a user, and provides a link and textual support for thevalue of the data point.

FIG. 8 is a detail view of a further aspect of the user interface,showing ratings at various hierarchical levels starting with a categoryand delving deeper down to data points of emission reduction (which inthis example is an “A” rating), and provides a of a particular outcome,all shown concurrently on the same user-interface display.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS OF THE INVENTION

By way of overview and introduction, the total CO2 emission of an entitycan be calculated in accordance with a broad aspect of the invention bycombining discrete estimates into a total estimate, wherein eachdiscrete estimate concerns a portion of the total estimate and uses acommon pool of data concerning the entity, and one of its industry,sub-sector or sector. In one embodiment, carbon estimates are made usingfour models, if underlying data is available to drive the algorithmsused by these models, and no less than two models. Optionally,additional models can be used.

The following discussion is made in regard to the entity being a companyof one sort or another, but it will be appreciated that the inventionapplies to entities of a variety of structures, including withoutlimitation, companies, corporations, limited liability companies,limited liability corporations, partnerships, limited liabilitypartnerships, self-regulated organizations, and joint ventures.

The estimates are based on variables that concern the company and anindustry, sector, or sub-sector to which the company belongs. The can beboth quantitative and qualitative. As shown in FIG. 1, certain variabletypes that can be used in estimating carbon production by a company,including geographic factors, emissions of various types, energyconsumption by the company, the number of employees, sales data for thecompany and company policies. Each of these data types provide indirectinformation that are combined by an algorithm, in accordance with theinvention, to arrive at several carbon dioxide emission estimates thatcan be further combined into a total carbon dioxide emission estimatefor the company.

The estimates are required to properly assess an investment risk in manycompanies due to incomplete reporting by many companies in many sectors.In some cases, there is no reporting at all which exposes investors andportfolio managers to risks based on uncertainties. In FIG. 2, a chartshows a trend across several sectors (based on data collected for the2006 calendar year) in which sectors that have large carbon emissionsgenerally have better reporting that sectors with comparatively lowercarbon emissions. What can be appreciated, however, is that no sector iscomplete in reporting its emissions data. The utility sector has thebest performance in this regard, at about 70% reporting whereascompanies that make consumer discretionary products have the lowestreporting performance.

In order to rate a company on a reasoned basis, data has to be used,whether it is quantitative or qualitative. Incomplete or missing datacauses difficulties in comparing companies on a fair basis to eachother, to their sector, and so on. One approach has been to assume thatif a company is not reporting the data, that the data is bad, but thatcan is skew a rating calculation and distort a rating that otherwisemight have been good. This is particularly true now that theenvironmental impact of a company is part of an assessment by manyinvestors and managers.

Referring briefly now to FIG. 3, a block diagram illustrates a computersystem 300 configured for employment of the methods described herein.System 300 includes a user interface 305, a processor 310, and a memory315. System 300 may be implemented on a general purpose microcomputer,such as one of the members of the Sun® Microsystems family of computersystems, one of the members of the IBM® Personal Computer family, one ofthe members of the Apple® Computer family, or a myriad otherconventional workstation, desktop computer, laptop computer, netbookcomputer, personal digital assistant, and smart phone devices that aregenerally available in the marketplace. Although system 300 isrepresented herein as a standalone system, it is not limited to such,but instead can be coupled to other computer systems via a network (notshown).

Memory 315 is a memory for storing data and instructions suitable forcontrolling the operation of processor 310. An implementation of memory315 would include a random access memory (RAM), a hard drive and a readonly memory (ROM). One of the components stored in memory 315 is aprogram 320.

Program 320 includes instructions for controlling processor 310 toexecute the methods described herein. Program 320 may be implemented asa single module 322 or as a plurality of modules 322A, 322B, . . . 322N(where N is an arbitrary integer) that operate in cooperation with oneanother. Program 320 is contemplated as representing a softwareembodiment of the method described hereinabove.

User interface 305 includes an input device, such as a keyboard, touchscreen, tablet, or speech recognition subsystem, for enabling a user tocommunicate information and command selections to processor 310. Userinterface 305 also includes an output device such as a display or aprinter. In the case of a touch screen, the input and output functionsare provided by the same structure. A cursor control such as a mouse,track-ball, or joy stick, allows the user to manipulate a cursor on thedisplay for communicating additional information and command selectionsto processor 310.

While program 320 is indicated as already loaded into memory 315, it maybe configured on a storage media 325 for subsequent loading into memory315. Storage media 325 can be any conventional storage media such as amagnetic tape, an optical storage media, a compact disc, or a floppydisc. Alternatively, storage media 325 can be a random access memory, orother type of electronic storage, located on a remote storage system.

In operation, the program 320 can take inputs from the memory 315 orfrom a remote data source. Also, the program can generate outputsthrough the user interface 305 or to a remote location via acommunication port 340. The communication port can exchange messages anddata using any number of conventional data transfer schemes andprotocols.

Referring now to FIG. 4, a system configuration 400 is illustrated inwhich a plurality of algorithms are each implemented as separate modules322A, 322B, 322C, and 322D (more generally referred to hereinbelow asmodules 322 unless specifics of a particular module are beingdescribed), with each module comprising code that executes in theprocessor 310. The modules 322 are each coupled to one or more datasources across a distributed computer network 410. The various datasources are schematically represented as a single database 420, but itis to be understood that the data can be collected and stored in asingle database, or can reside in multiple places that are accessible bythe distributed computer network 410.

The modules thus encode heuristics in accordance with different modelsthat can estimate carbon dioxide emissions, and each comprises code inthe memory 315 of the machine that has the processor 310. The modulessingularly or collectively implement one or more algorithms thattransform data received from the data source 420 into an estimate of thecarbon dioxide emission of the company under investigation (the“main-company”). A CO2 estimator module 324 includes code for all of themodels, though each model can be a separate module 322A-D (asillustrated). The processor executes the code to cause each model tocompute an estimate. Each model computes a different component of atotal CO2 emission estimate that can be output from the system 300 to auser machine 430 via a communicative connection 440 (e.g., as part ofthe content of a webpage or other file communicated to the user-machine.For example, a further module can comprise instructions executing in theprocessor that cause data to be transferred across the communicationport 340 so as to output the rating from the machine. The CO2 estimatormodule provides the results as a combined (e.g., a simple average),single output value. Thus, if the models comprise individual modules,the CO2 estimator receives their respective outputs and processes thatdata in order to provide a single output value.

In a preferred mode, the CO2 estimator module 324 operates to complementthe operation and output of a rating program 326 (which can be a secondmodule that is part of or distinct from the modules 322), such asdescribed in the aforementioned U.S. Pat. No. 7,277,864, which is herebyincorporated by reference as if set forth in its entirety herein. Thevalue(s) output by the CO2 estimator module 324 can be provided to therating program 326 via a connection 450. Meanwhile, a data-fault module328 operates as well to determine circumstances in which a particularcompany is missing or has incomplete CO2 emission data. The data-faultmodule causes the single output value of the CO2 estimator module 324 tobe presented to the user through a user interface at the user-machine430 when there is missing or incomplete CO2 emission data. Otherwise,the data-fault module will not present estimated values through theinterface, and, instead, the reported value for CO2 emission data forthe company will be presented. As such, the rating program 326 canidentify to the data-fault module 328 situations in which there ismissing or incomplete CO2 emission data such that a rating value for agiven parameter cannot be calculated. The data-fault module 328 canthereafter instruct the CO2 estimator 324 to provide or both compute andprovide an estimated carbon dioxide emission value to the rating programover line 450. The rating program 326 can then compute a rating of thecompany using the value supplied by the CO2 estimator 324 rather thanmake assumptions or fail due to the absence of or incomplete data.

It should be appreciated that the data-fault module 328 can beincorporated into the rating program 326, and that the CO2 estimator 324and any associated modules can be incorporated in the rating program asvariations within the scope of a code-driven embodiment.

With further reference now to the modules in FIG. 4, the CO2 estimator324 is illustrated as having four models encoded in respective modules:a “CO2 model” within module 322A, an “Energy model” within module 322B,a “Median model” within module 322C, and a “Policy model” within module322D. Each model returns one estimate, making a total of four estimatenumbers provided to the CO2 estimator 324. Not all are expected toprovide the same accuracy, and the following order should be but is notrequired to be respected. In particular, the “CO2” model should be usedif available as it provides “derived data.” Otherwise, and if available,the “Energy” model should be used as it also provides “derived data.”Next, otherwise, the “Median” model should be used as it provides“estimated data,” potentially together with the “Policy model” as itprovides an “adjustment.”

Any error associated with a particular model can be estimated by taking,for example, ten companies for which the data source 420 provides thetotal CO2 emissions, and by then applying each model/module 322 as ifthe total CO2 emissions were not known. The difference between theestimate and the reported number provides an idea of the errorassociated to the model. Preferably, the benchmarks used for computingthe error are for companies having known total CO2 emissions that are inthe same industry, sector or subsector as the company whose CO2 emissionis being estimated.

Module 322A includes instructions that implement a “CO2 model.” Theheuristics of this module are configured to provide a value that is partof the estimate for the total CO2 emissions of a company of interest. Inparticular, the instructions obtain, such as by a query of the datasource 420 via the communication port 340, the latest available totalCO2 emission for the company. For instance, this may be from theprevious calendar year, or from years ago, etc. This value is divided bythe number of employees that the company had during the same year as thelatest available total CO2 emission number. This provides a normalizedvalue of emissions per employee. Next, the normalized value ismultiplied by the number of employees of the company during the year inwhich we are calculating the CO2 emission estimate. In similar manner,the total CO2 emission number for the company is divided by the netsales figure reported by that company, in original currency, to providea normalized value of emissions per net sales which is then multipliedby the same net sales figure of the company for the year in which we arecalculating the CO2 emission estimate. The estimate provided from thisheuristic approach can be the average of the two numbers calculated asthe CO2 emission estimate, or can comprise one of these two numbers ifthe other cannot be calculated (e.g., if employee numbers are notreported, then the estimate provided by this module can comprise thenumber calculated using only net sales data).

By the CO2 model, the working assumption is that the magnitude of salesor employees bears a direct relationship to the CO2 emission value, suchthat a new estimate for CO2 emission in a given year can be “derived”from a known value in a prior year.

Module 322B includes instructions that implement an “energy model.” Theheuristics of this module are configured to provide a value that is partof the estimate for the total CO2 emissions of a company of interest,and like module 322A, derives an estimate from known values in prioryears. In particular, the instructions obtain, such as by a query of thedata source 420 via the communication port 340, the latest availabletotal energy purchase by the company in support of its operations. Forinstance, this may be from the previous calendar year, or from yearsago, etc. Note that for companies in the utility sector, the totalenergy sold is the metric to identify and use. This value is divided bythe number of employees that the company had during the same year as thelatest available total energy purchase number. This provides anormalized value of total energy purchased (or sold, in the case ofutilities) per employee for the main-company whose CO2 emission is to beestimated. Next, the same normalized values (ratios) are computed forall the other companies in the same industry (e.g., using 6 GICS digits)as the main-company, using total energy purchase data obtained from thedata source 420, as described above. If the number of available ratiosis smaller than 10, then the set of companies should be extended to thesub-sector (4 GICS digits). If the number of available ratios is stillsmaller than 10, then the set of companies should be extended to thesector (2 GICS digits). The reason for this is to provide a set ofratios for use in the next algorithmic step.

The algorithm continues by computing the percentile rank of thenormalized value for the main-company within and among the ratios forthe other companies whose normalized values were computed (i.e., for theother companies in the same industry, sector or subsector), referred toherein as the “comparison class.” Each member of the comparison classmust have a known total CO2 emission for the latest available year beinganalyzed for the main-company, as will be appreciated from thesubsequent steps performed by the algorithm. In any event, as a resultof this step, the algorithm identifies a rank-position of the maincompany relative to the comparison class.

Next, the total CO2 emissions output of the comparison class members isdivided by the number of employees to obtain a CO2 emission per employeevalue for the companies in the comparison class. These results areranked and the rank-position of the main-company is used to select a CO2emission per employee value of the company that matches themain-company's rank-position that was calculated in terms of energypurchased (or sold). This figure is multiplied by the number ofemployees of the main-company to provide a first (or only) component inthe CO2 estimate output by this module 322B.

The foregoing steps can then be repeated using net sales, in originalcurrency, instead of number of employees to arrive at a similar figurethat is defined in terms of net sales rather than in terms per employee.Specifically, the total energy purchased by the main-company is dividedby the net sales number for the latest-year having available data. Thesame metric is computed for each member of the comparison class. Themain-company's rank-position is determined in terms of its total energyusage, and then that rank-position is used to select a company in thecomparison class that has been ranked in terms of its CO2 emission peremployee. The company so-selected has its CO2 emission per employeemultiplied by the number of employees of the main-company to provide asecond (or only) component in the CO2 estimate output by this module322B.

The estimate output by the module 322B using the energy model can be theaverage of the components mentioned above, or can be the value of eithercomponent, but in either case is a derived value based on the assumptionthat energy purchases by a company can be correlated in comparison tocompanies in a comparison class with the CO2 emission amount ofcompanies in the comparison class, such that a new estimate for CO2emission in a given year can be “derived” from a known value of acomparison-class company in a prior year.

Module 322C includes instructions that implement a “median model.” Theheuristics of this module are configured to provide a value that is partof the estimate for the total CO2 emissions of a company of interest. Inparticular, the instructions obtain, such as by a query of the datasource 420 via the communication port 340, of the total CO2 emission forthe comparison class to the main-company for the same year that we areto compute a total CO2 estimate for the main-company. This data for eachof the comparison-class members is divided by the number of employees ofsuch companies (also obtained from the data source 420) to obtain a CO2emission per employee value for the comparison-class companies. Thealgorithm then computes a median value for the comparison class. Themedian value is multiplied by the number of employees of themain-company for the same year in question to provide a first (or only)component in the CO2 estimate output by this module 322C. For the sameyear in question, the CO2 emissions for the comparison class is dividedby their respective net sales (in original currency; obtained from thedata source 420) to obtain a CO2 emission per net sales value for thecomparison-class companies. The algorithm then computes a median valuefor the comparison class using this metric. The median value ismultiplied by the net sales number of the main-company for the same yearin question to provide a second (or only) component in the CO2 estimateoutput by this module 322C.

The estimate output by the module 322C using the median calculation canbe the average of the components mentioned above, or can be the value ofeither component, but in either case is an estimated value based on amedian calculation of CO2 emission among the comparison-class companiesand the assumption that the main-company's emission can be estimated inview of that figure.

Next, module 322D includes instructions that implement a “policy model”using heuristics configured to provide a value that is part of theestimate for the total CO2 emissions of a company of interest. Thepolicy module obtains from the data source 420 information sufficient toanswer the following policy-questions for the main-company and for thecomparison-class companies:

Parameter for Policy Question Algorithmic Processing Does the companyhave a policy to improve its energy En_En_RR_DP001[2] efficiency? Hasthere been a public commitment from a senior En_En_RR_DP003[2]management or board member to energy efficiency? Does the companydescribe, claim to have or mention En_En_RR_DP012[2] processes in placeto improve its energy efficiency? Does the company claim to use keyperformance En_En_RR_DP013[2] indicators (KPI) or the balanced scorecardto monitor energy efficiency? Does the company show through the use ofsurveys or En_En_RR_DP015[2] measurements that it is improving itsenergy efficiency? Has the company set targets or objectives to beachieved En_En_RR_DP019[2] on energy efficiency? Is the company makingprogress or succeeding to achieve En_En_RR_DP020[2] its previously setobjectives on energy efficiency? Does the company have a policy toreduce emissions? En_En_ER_DP001[1] Has there been a public commitmentfrom a senior En_En_ER_DP003[1] management or board member to emissionreduction? Does the company describe, claim to have or mentionEn_En_ER_DP005[1] processes in place to improve emission reduction? Doesthe company claim to use key performance En_En_ER_DP010[1] indicators(KPI) or the balanced scorecard to monitor emission reduction? Does thecompany show through the use of surveys or En_En_ER_DP012[1]measurements that it is reducing its emissions? Has the company settargets or objectives to be achieved En_En_ER_DP016[1] on emissionreduction? Is the company making progress or succeeding to achieveEn_En_ER_DP017[1] its previously set objectives on emission reduction?Does the company make direct use of renewable energy En_En_RR_DP046More specifically, the algorithm encoded in module 322D determines howmany of the policies noted above can be answered yes for themain-company and for the comparison-class companies. Next, the algorithmencoded in module 322D finds the percentile rank of the main-companyrelative to the comparison-class companies using the determination justmade regarding the policies. If the main-company is ranked less than the25% percentile, then the module 322D outputs a weighting or other factorto increase the estimate produced by the “median model” of module 322Cby 25%. On the other hand, if the main-company is ranked greater thanthe 25% percentile, then the module 322D outputs a weighting or otherfactor to decrease the estimate produced by the “median model” of module322C by 25%.

Referring now to FIGS. 5A-5C, a portion of a user-interface 500associated with the rating program 326 is illustrated as may be seen onthe user-machine 430 is illustrated in three parts for discussion,though in an implementation the UI 500, each of FIGS. 5A-5C can bedisplayed in a common window. By interacting with the user-interface500, such as using a conventional mouse device or other navigation tool,a user can manually drill-down to expose factors that bear on a ratingof the main-company such as by click-selecting a control such as outcomecollapse/expand control 510 (shown in the expanded state). In likemanner, the user can click-select to open and inspect drivers. For anexplanation of drivers and outcomes as they relate to rating companies,reference is made to the aforementioned U.S. patent that has beenincorporated by reference. In FIG. 5, the outcomes that are availablefor inspection are associated with the category of “emission reduction”and are one of several categories under the pillar of “environmental”factors. On the other hand, a user can submit via a form 520 and searchfor a particular pillar, category, driver, or outcome. Once a parameterof interest is selected, the middle section of the UI 500 (FIG. 5B)shows the data points whose values affect the main-company's rating. Theright section of the UI 500 (FIG. 5C) defines each data point andreveals the name of the parameter used for algorithmic processing.Depending on the implementation, the rating program or system may or maynot permit users to inspect down to the data point level. For instance,permission to inspect and or change the weightings, parameters reliedupon or the algorithm itself can be based on whether the end user is asubscriber to the system 300 or not. For more information on access tomultiple tiers of data, reference is made to U.S. patent applicationSer. No. 11/071,981, filed on Mar. 3, 2005, entitled Tiered Access ToIntegrated Rating System, which is hereby incorporated by reference asif set forth in its entirety herein.

In FIG. 5, the user has entered greenhouse gases as the search term inform 520 and that causes the emission reduction tab 530 to behighlighted as well as the leaf 540 which is the specific outcomeparameter relating to emission reduction/greenhouse gas emissions. FIG.6, shows a detail page 600 of a user interface for a company (BritishPetroleum PLC) including its rating 610, its standing compared to abenchmark 620, and its rank 630. The data points 640 that govern thevalue of the parameter (in this example, a “B-”), and the rules 650 thatdefine the calculation of the greenhouse gas emissions.

FIG. 7 illustrates further information that can be provided through theUI, such as at page 600 by scrolling downward, and illustrates theunderlying source document information including a link (e.g., a URL orhypertext link) to the source of the particular data point for thatparameter, and also textual documentation in support of the value ofthat parameter.

FIG. 8 illustrates the overall rating accorded to the category ofemission reduction (which in this example is an “A” rating), andprovides a breakdown of the ratings accorded to the drivers 820 andoutcomes 830. The outcome control 840 has been expanded to expose theindividual parameters such as greenhouse gas emissions 850, and therating for British Petroleum (“B-”) is shown highlighted to the user viathe user interface.

In operation, code executing in the machine that comprises thedata-fault module 328 has access to the foregoing data and modules 322(or to the output of the modules 322) and tests to determine if there isa value available on the basis of reported data. For instance, equation(1) below causes the reported value to be displayed through a userinterface on the user machine 430 if data is available both from therecords of the company under inspection (e.g., British Petroleum) and ifthird-party reported data is available and shows a non-zero salesfigure. In that case, the value is normalized to the sales figure.

if (isAvailable(@En_En_ER_DP023) and isAvailable(@Thomson_sales_USD) andnot @Thomson_sales_USD=0) then (1) (@En_En_ER_DP023/@Thomson_sales_USD)

The data-fault module can output the reported value in this instance.However, if the test performed is not satisfied, then the data-faultmodule will further process the data, such as using equation (2) belowto cause a CO2 total emission estimate to be populated in the databaseand presented through the user interface. An exemplary fault conditioncan process the information as follows:

else if (isAvailable(@En_En_ER_DPNNN) andisAvailable(@Thomson_sales_USD) and not @Thomson_sales_USD=0) then 2)(@En_En_ER_DPNNN/@Thomson_sales_USD)

In some situations, however, there may not be reported value oravailable third-party revenue information. In that circumstance, furtherprocessing by the data-fault module can be by code operative as follows:

else NA (3)

The present invention addresses the risks that investors are exposed todue to CO2 emissions by companies in their portfolios. The modulesdescribed herein operate within a rating system or in conjunction with arating program 326 as in the aforementioned patent to provideinstitutional investors with a tool through which they can assess thecarbon risk for companies within a portfolio, even in circumstances inwhich some of the data is incomplete or unreported. The modules use adiverse range of qualitative and quantitative sector based factors toarrive at values that are reliable and that can have any errorassociated with the individual models accounted for. By correcting forsuch errors at the model-level, the module(s) operate collectively toprovide an optimized routine for automated data estimation in theabsence of reported values. The estimates are fully transparent andenable clients to customize and create their own estimate models.

While the invention has been described in connection with a certainembodiment thereof, the invention is not limited to the describedembodiments but rather is more broadly defined by the recitations in theclaims below and equivalents thereof.

We claim:
 1. A method for outputting a rating based upon an estimate ofan environmental factor of an entity when data relating to theenvironmental factor for the entity is incomplete, the method beingimplemented in a machine having a processor and a memory, the methodcomprising: receiving at the machine data relating to an industry,sector, or sub-sector to which the entity belongs; determining by theprocessor one or more circumstances in which environmental factor dataconcerning the entity is at least one of missing and incomplete; basedon the determining, applying the data to a plurality of estimationmodules to obtain the estimate of the environmental factor of the entityin the absence of the missing or incomplete data, wherein eachestimation module of the plurality of estimation modules encodes arespective heuristic model algorithm; computing, by each estimationmodule of the plurality of estimation modules, using the respectiveheuristic model algorithm, a different component of the estimate of theenvironmental factor of the entity; applying by the processor theestimate as a factor in a formula to generate the rating for the entity;and outputting the rating from the machine.
 2. A method as in claim 1,further comprising making the estimate available to a user through auser interface.
 3. A method as in claim 2, wherein the making step isselectively enabled based upon operation of a data-fault moduleexecuting in the machine, the data-fault module being operative toconfigure the processor to enable the making step if the entity isdetermined to have missing or incomplete data concerning theenvironmental factor.
 4. A method as in claim 1, further comprising athreshold step of determining, via a data-fault module executing in themachine, whether the entity has missing or incomplete data concerningthe environmental factor, and performing the remaining steps if theentity is determined to have missing or incomplete data concerning theenvironmental factor.
 5. A method as in claim 1, wherein the step ofcomputing, by each estimation module of the plurality of estimationmodules, the different component of the estimate of the environmentalfactor of the entity is performed in parallel.
 6. A method as in claim1, further comprising combining the results of the plurality ofestimation modules.
 7. A method as in claim 6, wherein the combining theresults of the plurality of estimation modules includes computing asimple average that is provided as the environmental factor in the formof a single value.
 8. A method as in claim 1, wherein the respectiveheuristic model algorithms have a hierarchical accuracy order, andwherein the computing, by each estimation module of the plurality ofestimation modules, of the different component of the estimate isperformed in accordance with the hierarchical accuracy order.
 9. Amethod as in claim 1, including the additional steps of: computing anerror associated with each respective heuristic model algorithm byapplying each respective heuristic model algorithm to at least oneadditional entity having a known value concerning the environmentalfactor; and calculating a difference between the estimate for theadditional entity concerning the environmental factor and the knownvalue.
 10. A method as in claim 9, including the additional step ofcorrecting for any calculated difference concerning the additionalentity so as to account for such error in the estimate for the entity.11. A method as in claim 1, including the additional steps of: providinga user interface to the machine; and permitting users, through theuser-interface, to customize at least one of the respective heuristicmodel algorithms.
 12. A method as in claim 1, wherein the entity isselected from the group consisting essentially of a company, acorporation, a limited liability company, a limited liabilitycorporation, a partnership, a limited liability partnership, aself-regulated organization, and a joint venture.
 13. A method foroutputting an estimate of an environmental factor of an entity when datarelating to the environmental factor for the entity is incomplete, themethod being implemented in a machine having a processor and a memory,the method comprising: receiving at the machine data relating to anindustry, sector, or sub-sector to which the entity belongs; determiningby the processor one or more circumstances in which environmental factordata concerning the entity is at least one of missing and incomplete;based on the determining, applying the data to a plurality of estimationmodules, wherein each estimation module of the plurality of estimationmodules encodes a respective heuristic model algorithm; computing, byeach estimation module of the plurality of estimation modules, using therespective heuristic model algorithm, a different component of theestimate of the environmental factor of the entity; estimating by theprocessor the environmental factor of the entity based on the differentcomponents computed by the plurality of estimation modules; andoutputting the estimate from the machine.
 14. A method as in claim 13,further comprising making the estimate available to a user through auser interface.
 15. A method as in claim 14, wherein the making step isselectively enabled based upon operation of a data-fault moduleexecuting in the machine, the data-fault module being operative toconfigure the processor to enable the making step if the entity isdetermined to have missing or incomplete data concerning theenvironmental factor.
 16. A method as in claim 13, further comprising athreshold step of determining, via a data-fault module executing in themachine, whether the entity has missing or incomplete data concerningthe environmental factor, and performing the remaining steps if theentity is determined to have missing or incomplete data concerning theenvironmental factor.
 17. A method as in claim 13, wherein the step ofcomputing, by each estimation module of the plurality of estimationmodules, the different component of the estimate of the environmentalfactor of the entity is performed in parallel.
 18. A method as in claim13, wherein the estimating the environmental factor data includescombining the results of the plurality of estimation modules.
 19. Amethod as in claim 18, wherein the combining the results of theplurality of estimation modules includes computing a simple average thatis provided as the environmental factor in the form of a single value.20. A method as in claim 13, wherein the respective heuristic modelalgorithms have a hierarchical accuracy order, and wherein thecomputing, by each estimation module of the plurality of estimationmodules, the different component of the estimate is performed inaccordance with the hierarchical accuracy order.
 21. A method as inclaim 13, including the additional steps of: computing an errorassociated with each respective heuristic model algorithm by applyingeach respective heuristic model algorithm to at least one additionalentity having a known value concerning the environmental factor; andcalculating a difference between the estimate for the additional entityconcerning the environmental factor and the known value.
 22. A method asin claim 21, including the additional step of correcting for anycalculated difference concerning the additional entity so as to accountfor such error in the estimate for the entity.
 23. A method as in claim13, including the additional steps of: providing a user interface to themachine; and permitting users, through the user-interface, to customizeat least one of the respective heuristic model algorithms.
 24. A methodas in claim 13, wherein the entity is selected from the group consistingessentially of a company, a corporation, a limited liability company, alimited liability corporation, a partnership, a limited liabilitypartnership, a self-regulated organization, and a joint venture.